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The explanation of the chain rule with a schematic example of a neural net and calculations
at 27:15 onwards
https://youtu.be/uXt8qF2Zzfo?t=1635
And an explanation from a coder's perspective , in case you are fed up with people just blurting out the math because "that's how it works"
https://www.mql5.com/en/blogs/post/752198
PyTorch for Deep Learning & Machine Learning – Full Course (parts 11-16)
PyTorch for Deep Learning & Machine Learning – Full Course
Part 11
Part 12
Part 13
Part 14
Part 15
Part 16
PyTorch for Deep Learning & Machine Learning – Full Course (description of parts 17-22)
PyTorch for Deep Learning & Machine Learning – Full Course
Part 17
Part 18
learners to join in replicating the neural network in PyTorch code. The instructor then proceeds to build a tiny VGG convolutional neural network in PyTorch and explains that authors of research papers get to name new model architectures to make it easier for future reference. The code is initialized with input shape, hidden units, and output shape, which are typical parameters in building a PyTorch model.
Part 19
Part 20
Part 21
Part 22
PyTorch for Deep Learning & Machine Learning – Full Course (description of parts 23-26)
PyTorch for Deep Learning & Machine Learning – Full Course
Part 23
Part 24
Part 25
Part 26
No Black Box Machine Learning Course – Learn Without Libraries
No Black Box Machine Learning Course – Learn Without Libraries
00:00:00 - 01:00:00 In this YouTube video, the instructor presents a No Black Box Machine Learning Course that teaches how to code in machine learning without relying on libraries. The course covers topics related to building a web app that recognizes drawings, including data collection, feature extraction and visualization, and implementing classifiers such as the nearest neighbor and K nearest neighbor. The instructor emphasizes the importance of understanding data in machine learning and suggests resources for those who need to brush up on high school math and programming experience. The video demonstrates the process of creating a web page that acts as a data creator using JavaScript without any external libraries. The presenter also includes instructions on how to create an undo button and a name input field, store drawings in a data object, and save the paths on the user's computer. Finally, the video shows how to create a dataset generator in node.js and generate data associated with each sample using JavaScript.
01:00:00 - 02:00:00 In this YouTube video, the instructor teaches viewers how to create a machine learning dataset and extract features without using libraries. They demonstrate how to store the dataset in a folder that can communicate between node scripts and web apps and create a data viewer app. The instructor also shows how to visualize collected data using Google charts and how to identify and emphasize selected items in the chart and list. Overall, the video provides a comprehensive guide for learners to create machine learning datasets and extract features using only JavaScript.02:00:00 - 03:00:00 The "No Black Box Machine Learning Course – Learn Without Libraries" video demonstrates how to classify drawings based on their features without using machine learning libraries. The video creator emphasizes the importance of having a fast and responsive system for inspecting data to avoid manual errors. They demonstrate how to add features to the chart, how to hide the background, and how to display predicted labels on screen using dynamic containers with HTML and CSS. The video also covers data scaling techniques such as normalization and standardization. Finally, the video shows how to implement the K nearest neighbors classifier and count the number of each label within the K nearest neighbors.
03:00:00 - 03:50:00 The YouTube video "No Black Box Machine Learning Course - Learn Without Libraries" covers various topics related to K-nearest neighbor classification without using machine learning libraries such as JavaScript and Python. The video explains how to split data sets into training and testing sets, handle training and testing samples separately, and normalize the data. The instructor also discusses the importance of decision boundaries in understanding how a classifier operates, demonstrates how to implement a K-nearest neighbor (KNN) classifier in JavaScript, and generate a pixel-based plot without using machine learning libraries. Finally, the video ends with a call for viewers to explore additional capabilities of Python and reflect on what they've learned so far.
Part 1
Part 2
Part 3
Part 4
MIT 6.034 "Artificial Intelligence". Fall 2010. Lecture 1. Introduction and Scope
1. Introduction and Scope
This video is an introduction to the MIT 6.034 course "Artificial Intelligence" The professor explains the definition of artificial intelligence and its importance, and goes on to discuss the models of thinking and representations that are important for understanding the subject. Finally, the video provides a brief overview of the course, including how the grade is calculated and what the quiz and final will entail.
Lecture 2. Reasoning: Goal Trees and Problem Solving
2. Reasoning: Goal Trees and Problem Solving
This video discusses how to reasoning, goal trees, and problem solving. It introduces a technique called "problem reduction" and explains how it can be used to solve calculus problems. It also discusses how to use heuristic transformations to solve problems, and how knowledge can be used to solve problems in complex domains.
Lecture 3. Reasoning: Goal Trees and Rule-Based Expert Systems
3. Reasoning: Goal Trees and Rule-Based Expert Systems
This video explains how a rule-based expert system works. The system is designed to solve problems that are difficult to solve using more traditional methods. The system is composed of several rules that are connected by and gates, enabling the system to recognize a specific animal with certainty.
Lecture 4. Search: Depth-First, Hill Climbing, Beam
4. Search: Depth-First, Hill Climbing, Beam
In this YouTube video, Patrick Winston discusses different search algorithms, including Depth-first, Hill Climbing, Beam, and Best-first searches. Using a map as an example, he demonstrates the advantages and limitations of each algorithm and how understanding different search methods can improve problem-solving skills. Winston also discusses the application of search algorithms in intelligent systems, using the Genesis system to answer questions about the Macbeth story. He also introduces the concept of a Pyrrhic victory and how search programs can discover such situations by looking through graphs and reporting their findings in English. Overall, the video provides a comprehensive overview of search algorithms and their practical use in real-world scenarios.
Lecture 5. Search: Optimal, Branch and Bound, A*
5. Search: Optimal, Branch and Bound, A*
The video discusses several search algorithms for finding the shortest path between two places, focusing on the example of Route 66 between Chicago and Los Angeles. The video introduces the concept of heuristic distance and provides examples of different search algorithms, such as hill climbing, beam search, and branch and bound. The speaker emphasizes the importance of using admissible and consistent heuristics in the A* algorithm to optimize the search. Furthermore, the video notes the effectiveness of using an extended list and airline distances to determine lower bounds on the shortest path. Ultimately, the video concludes with the promise of discussing further refinements of the A* algorithm in the next lecture.